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lfq_fit(),
lfq_advantage(), lfq_forecast(),
lfq_score() enable tidyverse-style chaining with
|>.register_engine() /
unregister_engine() allow third-party packages to register
custom modeling engines, similar to the parsnip engine system.lfq_summary(): One-row-per-lineage
overview combining growth rates, confidence intervals, and relative Rt
in a single tibble.as.data.frame.lfq_data(): Clean tibble
export for interoperability.fit_model() now accepts both built-in and registered
engine names.lfq_engines() lists all available engines including
custom registrations.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.